複數模糊集成學習方法於趨勢預測之研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：191

、訪客IP：3.138.172.222

姓名

王慶豐(Ching-Feng Wang) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

複數模糊集成學習方法於趨勢預測之研究
(Complex Fuzzy Ensemble Learning Method for Trend Prediction)

相關論文

★ 變數選擇在智慧型系統與應用之研究	★ 智慧型系統之參數估測研究─一個新的DE方法
★ 合奏學習式智慧型系統在分類問題之研究	★ 複數模糊類神經系統於多類別分類問題之研究
★ 融入後設認知策略的複數模糊認知圖於分類問題之研究	★ 分類問題之研究－以複數型模糊類神經系統為方法
★ 智慧型差分自回歸移動平均模型於時間序列預測之研究	★ 計算智慧及複數模糊集於適應性影像處理之研究
★ 智慧型模糊類神經計算模式使用複數模糊集合與ARIMA模型	★ Empirical Study on IEEE 802.11 Wireless Signal – A Case Study at the NCU Campus
★ 自我建構式複數模糊ARIMA於指數波動預測之研究	★ 資料前處理之研究：以基因演算法為例
★ 針對文字分類的支援向量導向樣本選取	★ 智慧型區間預測之研究─以複數模糊類神經、支持向量迴歸、拔靴統計為方法
★ 複數模糊類神經網路在多目標財經預測	★ 智慧型模糊類神經計算使用非對稱模糊類神經網路系統與球型複數模糊集

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

面對現今的大數據時代，資料的價值需要由資訊技術不斷創造，甚至進一步地預測資料的發展趨勢，人工智慧中的深度學習即為當今預測的最佳工具之一。本研究提出一種新形態之複數模糊類神經分類模型 (Complex Neuro-Fuzzy Classification Model, CNFC)，透過複數高斯模糊集合的特性，模糊化輸入資料的類別隸屬程度，更加精確描述類別值域，增強模型的預測及應用能力。以減法分群演算法 (Subtractive Clustering Algorithm, SCA) 識別資料趨向類別，輔助模型進行動態式分類預測，其中採用粒子群最佳化演算法 (Particle Swarm Optimization, PSO) 與遞迴最小平方法 (Recursive Least Squares Estimator, RLSE) 為複合式最佳化演算法 (Hybrid optimization algorithm)，針對模型不同部分的參數進行優化，將有效提升模型優化效率。實驗透過重複性與集成學習方法進行多樣化的文獻模型效能比較，驗證CNFC的預測效能與PSO-RLSE的最佳化成效於股價時間序列資料具有較佳能力。

摘要(英)

Facing the current era of big data, the value of information is revealed constantly by information technologies, and even further predict the future trend of the data, deep learning in artificial intelligence is one of the best tools for current prediction. This study proposes a novel Complex Neuro-Fuzzy Classification Model (CNFC), through the characteristics of complex Gaussian fuzzy sets, the class degree of input data is fuzzified, which more accurately describes the class value and enhances the prediction and application ability of the model. Identify the directional classification of data by Subtractive Clustering Algorithm (SCA) and assisting models for dynamic classification prediction, the model uses Particle Swarm Optimization (PSO) and Recursive Least Squares Estimator (RLSE) as the hybrid optimization algorithm for parameters optimization of different parts of the model will effectively improve the efficiency of model optimization. The experiment verifies the predictive performance of CNFC and the optimization effect of PSO-RLSE have better ability in stock price time series data through repetitiveness and ensemble learning methods for a variety of literature model performance comparisons.

關鍵字(中)

★ 複數模糊類神經
★ 複數模糊集
★ 特徵選取
★ 粒子群最佳化演算法
★ 遞迴最小平方法
★ 集成學習

關鍵字(英)

★ complex neuro-fuzzy
★ complex fuzzy set
★ feature selection
★ particle swarm optimization
★ recursive least squares estimator
★ ensemble model

論文目次

中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
符號說明 viii
一、緒論 1
1-1 研究背景 1
1-2 研究動機與目的 1
1-3 研究方法概述 3
1-4 論文架構 3
二、文獻探討 5
2-1 分群演算法 5
2-2 特徵選取 5
2-3 模糊集合 6
2-4 機器學習最佳化演算法 7
三、研究方法 9
3-1 特徵選取 9
3-2 複數模糊集合 15
3-3 複數模糊類神經分類模型 17
3-4 PSO-RLSE複合式最佳化演算法 21
四、實驗 27
4-1 實驗一：日經平均指數時間序列分類預測 28
4-2 實驗二：韓國綜合股價指數時間序列分類預測 32
4-3 實驗三：美國標準普爾500指數時間序列分類預測 36
五、討論 41
六、結論與未來研究方向 44
6-1 結論 44
6-2 未來研究方向 44
參考文獻 46

參考文獻

[1] Engle, R.F., “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” ECONOMETRICA, Vol. 50, Issue 4, pp. 987-1007, 1982.
[2] Bollen, J., Mao, H. and Zeng, X., “Twitter mood predicts the stock market,” Journal of Computational Science, Vol. 2, Issue 1, pp. 1-8, 2011.
[3] Dimpfl, T. and Jank, S., “Can Internet Search Queries Help to Predict Stock Market Volatility?,” European Financial Management, Vol. 22, Issue 2, pp. 171-192, 2016.
[4] Patel, J., Shah, S., Thakkar, P. and Kotecha, K., “Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques,” Expert Systems with Applications, Vol. 42, Issue 1, pp. 259-268, 2015.
[5] Shannon, C.E. and Weaver, W., “The Mathematical Theory of Communication,” Univ of Illinois Press, 1949.
[6] Eberhart, R. and Kennedy, “A new optimizer using particle swarm theory,” IEEE International Symposium on Micro Machine and Human Science (Nagoya, Japan), pp. 39-43, 1995.
[7] Eberhart, R. and Kennedy, “Particle Swarm Optimization,” IEEE International Conference on Neural Networks (Perth, Australia), Vol. 4, pp. 1942-1948, 1995.
[8] Yager, R.R. and Filev, D.P., “Generation of Fuzzy Rules by Mountain Clustering,” Journal of Intelligent & Fuzzy Systems, Vol. 2, No. 3, pp. 209-219, 1994.
[9] Dunn, J. C., “A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters,” Journal of Cybernetics, Vol. 3, Issue. 3, pp. 32-57, 1973.
[10] Bezdek, J.C., “Cluster Validity with Fuzzy Sets,” J. Cybernet., Vol. 3, No. 3, pp. 58-72, 1974.
[11] Bezdek, J.C., Ehrlich, R. and Full, W., “FCM: The fuzzy c-means clustering algorithm,” Computers & Geosciences, Vol. 10, Issues 2–3, pp. 191-203, 1984.
[12] Bezdek, J.C., “Pattern Recognition with Fuzzy Objective Function Algorithms,” Springer Science & Business Media, 2013.
[13] Zheng, Y., Jeon, B., Xu, D., Wu, Q.M. and Zhang, H., “Image segmentation by generalized hierarchical fuzzy C-means algorithm,” Journal of Intelligent & Fuzzy Systems, Vol. 28, Issue 2, pp. 961-973, 2015.
[14] Guyon, I. and Elisseeff, A., “An Introduction to Variable and Feature Selection,” Journal of Machine Learning Research, pp. 1157-1182, 2003.
[15] Guyon, I., Weston, J., Barnhill, S. and Vapnik, V., “Gene Selection for Cancer Classification using Support Vector Machines,” Machine Learning, Vol. 46, Issue 1-3, pp. 389-422, 2002.
[16] Pearson, K., “X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling,” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, Vol. 50, Issue 302, pp. 157-175, 1900.
[17] Tibshirani, R., “Regression Shrinkage and Selection Via the Lasso,” Journal of the Royal Statistical Society: Series B (Methodological), Vol. 58, Issue 1, pp. 267-288, 1996.
[18] Tsai, C.F. and Hsiao, Y.C., “Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches,” Decision Support Systems, Vol. 50, Issue 1, pp. 258-269, 2010.
[19] Zadeh, L.A., “Fuzzy Sets,” Information and Control, Vol. 8, Issue 3, pp. 338-353, 1965.
[20] Cantor, G., “Ueber eine Eigenschaft des Inbegriffs aller reellen algebraischen Zahlen,” Journal für die reine und angewandte Mathematik, pp. 252-268, 1874.
[21] Nauck, D. and Kruse, R., “Neuro-fuzzy systems for function approximation,” Fuzzy Sets and Systems, Vol. 101, Issue 2, pp. 261-271, 1999.
[22] Manogaran, G., Varatharajan, R. and Priyan, M.K., “Hybrid Recommendation System for Heart Disease Diagnosis based on Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System,” Multimedia Tools and Applications, Vol. 77, Issue 4, pp. 4379-4399, 2018.
[23] McCulloch, W.S. and Pitts, W., “A logical calculus of the ideas immanent in nervous activity,” The bulletin of mathematical biophysics, Vol. 5, Issue 4, pp. 115-133, 1943.
[24] Rosenblatt, F., “The Perceptron — A Perceiving and Recognizing Automaton,” Cornell Aeronautical Laboratory, 1957.
[25] Khosravi, A., Koury, R.N.N., Machado, L. and Pabon, J.J.G., “Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system,” Sustainable Energy Technologies and Assessments, Vol. 25, Issue 4, pp. 146-160, 2018.
[26] Hasanipanah, M., Amnieh, H.B., Arab, H. and Zamzam, M.S., “Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting,” Neural Computing and Applications, Vol. 30, Issue 4, pp. 1015-1024, 2018.
[27] Colorni, A., Dorigo, M. and Maniezzo, V., “Distributed Optimization by Ant Colonies,” Proceedings of the 1st European Conference on Artificial Life, Vol. 142, pp. 134-142, Paris, 1992.
[28] Ramot, D., Milo, R., Friedman, M. and Kandel, A., “Complex fuzzy sets,” IEEE Transactions on Fuzzy Systems, Vol. 10, No. 2, pp. 171-186, 2002.
[29] Takagi, T. and Sugeno, M., “Fuzzy Identification of Systems and Its Applications to Modeling and Control,” IEEE Systems, Man, and Cybernetics Society, Vol. 15, Issue 1, pp. 116-132, 1985.
[30] Tu, C.H. and Li, C., “A Novel Entropy-Based Approach to Feature Selection,” Asian Conference on Intelligent Information and Database Systems, pp. 445-454, Springer, Cham, 2017.
[31] Girolami, M. and He, C., “Probability density estimation from optimally condensed data samples,” IEEE Transactions on pattern analysis and machine intelligence, Vol. 25, Issue 10, pp. 1253-1264, 2003.
[32] Parzen, E., “On Estimation of a Probability Density Function and Mode,” The Annals of Mathematical Statistics, Vol. 33, Issue. 3, pp. 1065-1076, 1962.
[33] Li, C. and Chiang, T.W., “Complex Neurofuzzy ARIMA Forecasting—A New Approach Using Complex Fuzzy Sets,” IEEE Transactions on Fuzzy Systems, Vol. 21, Issue 3, pp. 567-584, 2013.
[34] 王伯倫，李俊賢，「高斯分布鯨群演算法於最佳化問題之研究」，(to be submitted for publication)，2019.
[35] Kearns, M. and Valiant, L., “Cryptographic limitations on learning Boolean formulae and finite automata,” Journal of the ACM (JACM), Vol. 41, Issue 1, pp.67-95, 1994.
[36] Breiman, L., “Bagging predictors,” Machine Learning, Vol. 24, Issue 2, pp. 123-140,1996.
[37] Schapire, R.E., “The strength of weak learnability,” Machine Learning, Vol. 5, Issue 2, pp. 197-227, 1990.
[38] Freund, Y. and Schapire, R.E., “A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting,” Journal of computer and system science, Vol. 55, Issue 1, pp.119-139, 1997.
[39] Gama, J.M.P.D., “Combining Classification Algorithms,” 1999.
[40] Hansen, L.K. and Salamon, P., “Neural Network Ensembles,” IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol.12, pp.993-1001, 1990.
[41] Huang, W., Nakamori, Y. and Wang, S.Y., “Forecasting stock market movement direction with support vector machine,” Computers & Operations Research, Vol. 32, Issue 10, pp.2513-2522, 2005.
[42] Leung, M.T., Daouk, H. and Chen, A.S., “Forecasting stock indices: a comparison of classification and level estimation models,” International Journal of Forecasting, Vol. 16, Issue 2, pp. 173-190, 2000.
[43] Kim, K.J. and Han, I., “Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index,” Expert Systems with Applications, Vol. 19, Issue 2, pp. 125-132, 2000.
[44] Wang, Y., “Stock price direction prediction by directly using prices data: an empirical study on the KOSPI and HIS,” International Journal of Business Intelligence and Data Mining, Vol. 9, No. 2, pp. 145-160, 2014.
[45] Lahmiri, S., “Forecasting Direction of the S&P500 Movement Using Wavelet Transform and Support Vector Machines,” International Journal of Strategic Decision Sciences (IJSDS), Vol. 4, Issue 1, pp. 79-89, 2013.
[46] Tsaih, R., Hsu, Y. and Lai, C.C., “Forecasting S&P 500 stock index futures with a hybrid AI system,” Decision Support Systems, Vol. 23, Issue 2, pp. 161-174, 1998.
[47] Shen, S., Jiang, H. and Zhang, T., “Stock Market Forecasting Using Machine Learning Algorithms,” Department of Electrical Engineering, Stanford University, Stanford, CA, pp. 1-5, 2012.

指導教授

李俊賢(Chunshein Li)

審核日期

2019-7-16

推文